Prototype Decision-Support System for Designing and Costing Municipal Green Infrastructure
Bibliographic record
Abstract
There is growing momentum across many municipal jurisdictions in North America to reuse public and privately held vacant and underutilized urban land on a temporary to potentially permanent basis for community-centered and community-driven projects. Some uses include urban agriculture, parks and open spaces, and linear bikeway or walkway connections. Across many jurisdictions, limited resources have been allocated to inventorying and determining the valuation of these urban assets and their potential to contribute to a city’s green infrastructure capacity. The purpose of this research is to add an augmented capacity to an existing Microsoft Excel-based decision-support tool that captures the condition and location of vacant and underutilized land, calculates the relative suitability of the inventoried land for a suite of reuse strategies, and allows the user to evaluate location-allocation modeling scenarios. The additional capacity introduced herein provides users with the ability to produce a scaled design drawing for each allocated reuse strategy, and subsequently perform a life-cycle cost analysis (LCCA) based on user-defined design scenarios. The application of the design and costing tool, known as DECO, to a portion of an underutilized hydro utility corridor in Hamilton, Ontario, Canada, is presented and discussed to demonstrate the usability and inherent benefits of a graphically based LCCA approach. While developed as a decision-support tool for application by community groups, DECO has the potential to assist municipal planning staff and private and public land owners in clarifying the trade-offs between various design alternatives, given a specified life-cycle length. DECO is designed to allow the user to perform a series of “what-if” scenarios/sensitivity analyses to aid in well-informed green infrastructure investment decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".